A Clustering Approach for the l-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

Abstract

In privacy preserving data mining, the -diversity and -anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, -diversity model gives better privacy and lesser information loss as compared to the -anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, -means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others exceptmeans. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with -diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms.

Severity detection and infection level identification of tuberculosis using deep learning

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“…This algorithm produces a smoother variation and long term memory effect. According to the fractional calculus, 34 D χ [ X x , y + 1 ] = X x , y + 1 − X x , y .…”

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

“…The proposed FJO algorithm is the integration of the fractional calculus 39 with the Jaya Optimization Algorithm (JOA), 40 and hence, acquires the benefits of fractional theory in the standard JOA. The proposed FJO algorithm is the integration of the fractional calculus 39 with the Jaya Optimization Algorithm (JOA), 40 and hence, acquires the benefits of fractional theory in the standard JOA.…”

“…The proposed FJO algorithm derives the optimal weights to tune the DCNN classifier involved in categorizing the severity level of the brain tumors. The proposed FJO algorithm is the integration of the fractional calculus 39 with the Jaya Optimization Algorithm (JOA), 40 algorithm works in a single phase, and hence, JOA is simpler in operation. The algorithm aims to get closer to the best solution, and hence, the name "Jaya," which means victory.…”

“…To integrate the fractional theory in the JOA, Equation (22) is modified as where ∂ α W τ + 1 i, j h i refers to the term determined using fractional calculus. 39 The fractional calculus improves the convergence and optimization speed of JOA. Generally, it is known that the fractional calculus boosts the performance of the algorithms, such as modeling, edge detection, and so on, through solving the derivative and integral equations.…”